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7d68525
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1 Parent(s): 3952f28

Update app.py

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Files changed (1) hide show
  1. app.py +10 -2
app.py CHANGED
@@ -146,7 +146,7 @@ def process_and_compare(file1, sheet1, file2, sheet2):
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  plt.savefig(file_path, format='png', bbox_inches='tight')
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  plt.close()
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- return file_path, gr.update(choices=stored_df1.Country.values.tolist())
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  def find_sentences_with_keywords(text, keywords):
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  # Split text into sentences using regular expression to match sentence-ending punctuation
@@ -222,6 +222,8 @@ def generate_text(df, country, theme):
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  Here is another table:
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  {row_str}
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  Summarize the adverse scenario growth for {theme} in {country} based on the data above, following a similar pattern to the example for France.
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  """
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@@ -327,9 +329,15 @@ with gr.Blocks() as demo:
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  outputs=text_result_df1)
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  with gr.Column():
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  sentiment_results_pdf2 = gr.HighlightedText(label="Sentiment Analysis - PDF 2")
 
 
 
 
 
 
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  # Button to extract text from PDFs and perform sentiment analysis
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- b1.click(fn=process_and_compare, inputs=[file1, sheet, file2, sheet], outputs=[result,country_1_dropdown])
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  b2.click(fn=process_pdfs_and_analyze_sentiment, inputs=[file1, file2, sheet], outputs=[sentiment_results_pdf1, sentiment_results_pdf2])
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  plt.savefig(file_path, format='png', bbox_inches='tight')
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  plt.close()
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+ return file_path, gr.update(choices=stored_df1.Country.values.tolist()), gr.update(choices=stored_df2.Country.values.tolist())
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  def find_sentences_with_keywords(text, keywords):
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  # Split text into sentences using regular expression to match sentence-ending punctuation
 
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  Here is another table:
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  {row_str}
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+ The theme is {theme}
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+
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  Summarize the adverse scenario growth for {theme} in {country} based on the data above, following a similar pattern to the example for France.
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  """
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  outputs=text_result_df1)
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  with gr.Column():
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  sentiment_results_pdf2 = gr.HighlightedText(label="Sentiment Analysis - PDF 2")
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+ country_2_dropdown = gr.Dropdown(label="Select Country from Excel File 2")
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+ summarize_btn2_country = gr.Button("Summary for the selected country")
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+ text_result_df2 = gr.Textbox(label="Sentence for excel file 2", lines=2)
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+ summarize_btn2_country.click(fn=lambda country, theme: generate_text(stored_df2, country, theme),
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+ inputs=[country_2_dropdown, sheet],
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+ outputs=text_result_df2)
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  # Button to extract text from PDFs and perform sentiment analysis
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+ b1.click(fn=process_and_compare, inputs=[file1, sheet, file2, sheet], outputs=[result,country_1_dropdown, country_2_dropdown])
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  b2.click(fn=process_pdfs_and_analyze_sentiment, inputs=[file1, file2, sheet], outputs=[sentiment_results_pdf1, sentiment_results_pdf2])
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